International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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TSCH-Based Scheduling of IEEE 802.15.4e in Coexistence with Interference Network Cluster: A DNN Approach

  • Haque, Md. Niaz Morshedul (Department of Electrical, Electronic and Computer Engineering, University of Ulsan) ;
  • Koo, Insoo (Department of Electrical, Electronic and Computer Engineering, University of Ulsan)
  • Received : 2021.11.11
  • Accepted : 2021.11.18
  • Published : 2022.02.28
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Abstract

In the paper, we propose a TSCH-based scheduling scheme for IEEE 802.15.4e, which is able to perform the scheduling of its own network by avoiding collision from interference network cluster (INC). Firstly, we model a bipartite graph structure for presenting the slot-frame (channel-slot assignment) of TSCH. Then, based on the bipartite graph edge weight, we utilize the Hungarian assignment algorithm to implement a scheduling scheme. We have employed two features (maximization and minimization) of the Hungarian-based assignment algorithm, which can perform the assignment in terms of minimizing the throughput of INC and maximizing the throughput of own network. Further, in this work, we called the scheme "dual-stage Hungarian-based assignment algorithm". Furthermore, we also propose deep learning (DL) based deep neural network (DNN)scheme, where the data were generated by the dual-stage Hungarian-based assignment algorithm. The performance of the DNN scheme is evaluated by simulations. The simulation results prove that the proposed DNN scheme providessimilar performance to the dual-stage Hungarian-based assignment algorithm while providing a low execution time.

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Acknowledgement

This work was supported in part by the National Research Foundation of Korea through the Korean Government Ministry of Science and ICT (MSIT) under Grant NRF-2021R1A2B5B01001721. In addition, these results were supported by "Regional Innovation Strategy (RIS)" through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(MOE)(2021RIS-003)

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